Our conditional kernel strategy for modelling \(\lbrace Z_t\rbrace \)  appears to perform well. However, we could alternatively model the extremal behaviour of \(W_t\)  using the approaches described in [1]} and [2]}, whereby a GPD model is defined above a high threshold and the extremal temporal dependence structure is modelled using a \(k\) th order extremal Markov process. This approach stems from the conditional multivariate extreme value methodology of [3]}. We did not implement this approach as part of this study due to the additional complexity involved and that extrapolation occurred naturally through the features of our model. In addition, the observed values of \(W_t\)  correspond to upper tail values of \(X_i\) , the distribution of wind speeds in cell \(i\) . Because we have a large dataset of observations in the upper tail, we felt that standard statistical modelling approaches were sufficient in this case. However, investigation of the benefits of imposing an extremal temporal dependence structure on the upper tail of \(W_t\)  represents an interesting avenue of future research.
